Sales statistics: who comes back and when they buy
Cohorts show loyalty month by month, and the timing maps show when buyers actually click "Buy now". Set your packing shifts and ad schedule on facts, not hunches.
Cohorts: who returns after the first purchase
Every acquisition month is a cohort. You see what share of that month's buyers came back in the following months: M+1, M+2 and beyond. The darker the cell, the stronger the return. The most honest loyalty measure there is.
- 13 months of cohorts with each one's size
- Return percentage in the following months
- An intensity scale: patterns pop instantly
- M+0 = the first-order month
| Cohort | Size | M+0 | M+1 | M+2 | M+3 | M+4 | M+5 | M+6 |
|---|---|---|---|---|---|---|---|---|
| 2026-08 | 168 | 100% | ||||||
| 2026-07 | 356 | 100% | 2% | |||||
| 2026-06 | 342 | 100% | 2% | 1% | ||||
| 2026-05 | 371 | 100% | 1% | 2% | 1% | |||
| 2026-04 | 402 | 100% | 2% | 3% | 2% | 1% | ||
| 2026-03 | 236 | 100% | 2% | 3% | 1% | 2% | 1% | |
| 2026-02 | 154 | 100% | 1% | 2% | 5% | 2% | 2% | 1% |
Retention:≤1%2%3%5%+100%
Order timing by hour of week
A 7-day × 24-hour heatmap shows when buyers click "Buy now" (Warsaw time). Match packing shifts and campaign schedules to the hot spots, and leave the night hours alone.
- A 7×24 heatmap from 12 months of orders
- Order totals per day of week
- Hot spots for ad scheduling
- Europe/Warsaw time
| 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mon | 2 | 4 | 1 | 4 | 3 | 3 | 10 | 14 | 26 | 30 | 31 | 36 | 33 | 35 | 32 | 29 | 33 | 33 | 37 | 34 | 31 | 31 | 21 | 12 | 525 |
| Tue | 4 | 1 | 3 | 1 | 5 | 5 | 7 | 16 | 23 | 32 | 33 | 33 | 35 | 32 | 34 | 31 | 30 | 35 | 34 | 36 | 33 | 28 | 23 | 9 | 523 |
| Wed | 6 | 3 | 3 | 2 | 7 | 9 | 13 | 25 | 29 | 35 | 35 | 32 | 34 | 31 | 33 | 32 | 32 | 36 | 33 | 35 | 30 | 20 | 11 | 526 | |
| Thu | 3 | 5 | 2 | 4 | 4 | 11 | 15 | 22 | 30 | 31 | 36 | 33 | 30 | 32 | 29 | 33 | 33 | 32 | 34 | 31 | 31 | 22 | 8 | 511 | |
| Fri | 5 | 2 | 4 | 2 | 1 | 6 | 8 | 17 | 24 | 28 | 34 | 34 | 36 | 33 | 30 | 32 | 31 | 36 | 35 | 32 | 34 | 29 | 24 | 10 | 527 |
| Sat | 2 | 4 | 1 | 4 | 3 | 3 | 10 | 14 | 25 | 29 | 30 | 35 | 32 | 34 | 31 | 28 | 32 | 32 | 36 | 33 | 30 | 30 | 20 | 12 | 510 |
| Sun | 4 | 1 | 3 | 1 | 5 | 5 | 7 | 16 | 23 | 33 | 34 | 34 | 36 | 33 | 35 | 32 | 31 | 36 | 35 | 37 | 34 | 29 | 23 | 9 | 536 |
Intensity: quietpeak
Orders by day of month
Average orders for each day of the month across the last 12 months, normalized: day 31 only occurs ~7 times a year, so the numbers are comparable with day 15. Check whether "after payday" is a pattern or a myth for your store.
- Average per occurrence, not a raw sum
- Days 29–31 normalized
- 12 months of data in one view
See your own patterns in SellerKokpit
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